Degenerate T-cell recognition of peptides on MHC molecules creates large holes in the T-cell repertoire

Predicting T cell receptor functionality against mutant epitopes

Cancer cells and pathogens can evade T cell receptors (TCRs) via mutations in immunogenic epitopes. TCR cross-reactivity (i.e., recognition of multiple epitopes with sequence similarities) can counteract such escape but may cause severe side effects in cell-based immunotherapies through targeting self-antigens. To predict the effect of epitope point mutations on T cell functionality, we here present the random forest-based model Predicting T Cell Epitope-Specific Activation against Mutant Versions (P-TEAM). P-TEAM was trained and tested on three datasets with TCR responses to single-amino-acid mutations of the model epitope SIINFEKL, the tumor neo-epitope VPSVWRSSL, and the human cytomegalovirus antigen NLVPMVATV, totaling 9,690 unique TCR-epitope interactions. P-TEAM was able to accurately classify T cell reactivities and quantitatively predict T cell functionalities for unobserved single-point mutations and unseen TCRs. Overall, P-TEAM provides an effective computational tool to study T cell responses against mutated epitopes.

A journey to your self: The vague definition of immune self and its practical implications

The identification of immunogenic peptides has become essential in an increasing number of fields in immunology, ranging from tumor immunotherapy to vaccine development. The nature of the adaptive immune response is shaped by the similarity between foreign and self-protein sequences, a concept extensively applied in numerous studies. Can we precisely define the degree of similarity to self? Furthermore, do we accurately define immune self? In the current work, we aim to unravel the conceptual and mechanistic vagueness hindering the assessment of self-similarity. Accordingly, we demonstrate the remarkably low consistency among commonly employed measures and highlight potential avenues for future research.

Integration of the immune memory into the pathogen-driven MHC polymorphism hypothesis

Major histocompatibility complex (MHC) genes (referred to as human leukocyte antigen or HLA in humans) are a key component of vertebrate immune systems, coding for proteins which present antigens to T-cells. These genes are outstanding in their degree of polymorphism, with important consequences for human and animal health. The polymorphism is thought to arise from selection pressures imposed by pathogens on MHC allomorphs, which differ in their antigen-binding capacity. However, the existing theory has not considered MHC selection in relation to the formation of immune memory. In this paper, we argue that this omission limits our understanding of the evolution of MHC polymorphism and its role in disease. We review recent evidence that has emerged from the massive research effort related to the SARS-CoV-2 pandemics, and which provides new evidence for the role of MHC in shaping immune memory. We then discuss why the inclusion of immune memory within the existing theory may have non-trivial consequence for our understanding of the evolution of MHC polymorphism. Finally, we will argue that neglecting immune memory hinders our interpretation of empirical findings, and postulate that future studies focusing on pathogen-driven MHC selection would benefit from stratifying the available data according to the history of infection (and vaccination, if relevant).

A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity

Antigen immunogenicity and the specificity of binding of T-cell receptors to antigens are key properties underlying effective immune responses. Here we propose diffRBM, an approach based on transfer learning and Restricted Boltzmann Machines, to build sequence-based predictive models of these properties. DiffRBM is designed to learn the distinctive patterns in amino-acid composition that, on the one hand, underlie the antigen's probability of triggering a response, and on the other hand the T-cell receptor's ability to bind to a given antigen. We show that the patterns learnt by diffRBM allow us to predict putative contact sites of the antigen-receptor complex. We also discriminate immunogenic and non-immunogenic antigens, antigen-specific and generic receptors, reaching performances that compare favorably to existing sequence-based predictors of antigen immunogenicity and T-cell receptor specificity.

Idiotope-Driven T-Cell/B-Cell Collaboration-Based T-Cell Epitope Prediction Using B-Cell Receptor Repertoire Sequences in Infectious Diseases

T-cell recognition of antigen epitopes is a crucial step for the induction of adaptive immune responses, and the identification of such T-cell epitopes is, therefore, important for understanding diverse immune responses and controlling T-cell immunity. A number of bioinformatic tools exist that predict T-cell epitopes; however, many of these methods highly rely on evaluating conventional peptide presentation by major histocompatibility complex (MHC) molecules, but they ignore epitope sequences recognized by T-cell receptor (TCR). Immunogenic determinant idiotopes are present on the variable regions of immunoglobulin molecules expressed on and secreted by B-cells. In idiotope-driven T-cell/B-cell collaboration, B-cells present the idiotopes on MHC molecules for recognition by idiotope-specific T-cells. According to the idiotype network theory formulated by Niels Jerne, such idiotopes found on anti-idiotypic antibodies exhibit molecular mimicry of antigens. Here, by combining these concepts and defining the patterns of TCR-recognized epitope motifs (TREMs), we developed a T-cell epitope prediction method that identifies T-cell epitopes derived from antigen proteins by analyzing B-cell receptor (BCR) sequences. This method allowed us to identify T-cell epitopes that contain the same TREM patterns between BCR and viral antigen sequences in two different infectious diseases caused by dengue virus and SARS-CoV-2 infection. The identified epitopes were among the T-cell epitopes detected in previous studies, and T-cell stimulatory immunogenicity was confirmed. Thus, our data support this method as a powerful tool for the discovery of T-cell epitopes from BCR sequences.

Determinants of tumor immune evasion: the role of T cell exposed motif frequency and mutant amino acid exposure

Few neoepitopes detected in tumor biopsies are immunogenic. Tumor-specific T cell responses require both the presentation of an epitope that differs from wildtype and the presence of T cells with neoepitope-cognate receptors. We show that mutations detected in tumor biopsies result in an increased frequency of rare amino acid combinations compared to the human proteome and gastrointestinal microorganisms. Mutations in a large data set of oncogene and tumor suppressor gene products were compared to wildtype, and to the count of corresponding amino acid motifs in the human proteome and gastrointestinal microbiome. Mutant amino acids in T cell exposed positions of potential neoepitopes consistently generated amino acid motifs that are less common in both proteome reference datasets. Approximately 10% of the mutant amino acid motifs are absent from the human proteome. Motif frequency does not change when mutants were positioned in the MHC anchor positions hidden from T cell receptors. Analysis of neoepitopes in GBM and LUSC cases showed less common T cell exposed motifs, and HLA binding preferentially placing mutant amino acids in an anchor position for both MHC I and MHC II. Cross-presentation of mutant exposed neoepitopes by MHC I and MHC II was particularly uncommon. Review of a tumor mutation dataset known to generate T cell responses showed immunogenic epitopes were those with mutant amino acids exposed to the T cell receptor and with exposed pentamer motifs present in the human and microbiome reference databases. The study illustrates a previously unrecognized mechanism of tumor immune evasion, as rare T cell exposed motifs produced by mutation are less likely to have cognate T cells in the T cell repertoire. The complex interactions of HLA genotype, binding positions, and mutation specific changes in T cell exposed motif underscore the necessity of evaluating potential neoepitopes in each individual patient.

A gut microbial peptide and molecular mimicry in the pathogenesis of type 1 diabetes

Type 1 diabetes (T1D) is an autoimmune disease characterized by the destruction of pancreatic β-cells. One of the earliest aspects of this process is the development of autoantibodies and T cells directed at an epitope in the B-chain of insulin (insB:9-23). Analysis of microbial protein sequences with homology to the insB:9-23 sequence revealed 17 peptides showing >50% identity to insB:9-23. Of these 17 peptides, the hprt4-18 peptide, found in the normal human gut commensal Parabacteroides distasonis, activated both human T cell clones from T1D patients and T cell hybridomas from nonobese diabetic (NOD) mice specific to insB:9-23. Immunization of NOD mice with P. distasonis insB:9-23 peptide mimic or insB:9-23 peptide verified immune cross-reactivity. Colonization of female NOD mice with P. distasonis accelerated the development of T1D, increasing macrophages, dendritic cells, and destructive CD8⁺ T cells, while decreasing FoxP3⁺ regulatory T cells. Western blot analysis identified P. distasonis-reacting antibodies in sera of NOD mice colonized with P. distasonis and human T1D patients. Furthermore, adoptive transfer of splenocytes from P. distasonis-treated mice to NOD/SCID mice enhanced disease phenotype in the recipients. Finally, analysis of human children gut microbiome data from a longitudinal DIABIMMUNE study revealed that seroconversion rates (i.e., the proportion of individuals developing two or more autoantibodies) were consistently higher in children whose microbiome harbored sequences capable of producing the hprt4-18 peptide compared to individuals who did not harbor it. Taken together, these data demonstrate the potential role of a gut microbiota-derived insB:9-23-mimic peptide as a molecular trigger of T1D pathogenesis.

Degeneracy measures in biologically plausible random Boolean networks

Background

Degeneracy—the ability of structurally different elements to perform similar functions—is a property of many biological systems. Highly degenerate systems show resilience to perturbations and damage because the system can compensate for compromised function due to reconfiguration of the underlying network dynamics. Degeneracy thus suggests how biological systems can thrive despite changes to internal and external demands. Although degeneracy is a feature of network topologies and seems to be implicated in a wide variety of biological processes, research on degeneracy in biological networks is mostly limited to weighted networks. In this study, we test an information theoretic definition of degeneracy on random Boolean networks, frequently used to model gene regulatory networks. Random Boolean networks are discrete dynamical systems with binary connectivity and thus, these networks are well-suited for tracing information flow and the causal effects. By generating networks with random binary wiring diagrams, we test the effects of systematic lesioning of connections and perturbations of the network nodes on the degeneracy measure.

Results

Our analysis shows that degeneracy, on average, is the highest in networks in which ~ 20% of the connections are lesioned while 50% of the nodes are perturbed. Moreover, our results for the networks with no lesions and the fully-lesioned networks are comparable to the degeneracy measures from weighted networks, thus we show that the degeneracy measure is applicable to different networks.

Conclusions

Such a generalized applicability implies that degeneracy measures may be a useful tool for investigating a wide range of biological networks and, therefore, can be used to make predictions about the variety of systems’ ability to recover function.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04601-5.

Mechanistic diversity in MHC class I antigen recognition

Throughout its evolution, the human immune system has developed a plethora of strategies to diversify the antigenic peptide sequences that can be targeted by the CD8+ T cell response against pathogens and aberrations of self. Here we provide a general overview of the mechanisms that lead to the diversity of antigens presented by MHC class I complexes and their recognition by CD8+ T cells, together with a more detailed analysis of recent progress in two important areas that are highly controversial: the prevalence and immunological relevance of unconventional antigen peptides; and cross-recognition of antigenic peptides by the T cell receptors of CD8+ T cells.

Immunoinformatics Analysis of SARS-CoV-2 ORF1ab Polyproteins to Identify Promiscuous and Highly Conserved T-Cell Epitopes to Formulate Vaccine for Indonesia and the World Population

SARS-CoV-2 and its variants caused the COVID-19 pandemic. Vaccines that target conserved regions of SARS-CoV-2 and stimulate protective T-cell responses are important for reducing symptoms and limiting the infection. Seven cytotoxic (CTL) and five helper T-cells (HTL) epitopes from ORF1ab were identified using NetCTLpan and NetMHCIIpan algorithms, respectively. These epitopes were generated from ORF1ab regions that are evolutionary stable as reflected by zero Shannon's entropy and are presented by 56 human leukocyte antigen (HLA) Class I and 22 HLA Class II, ensuring good coverage for the Indonesian and world population. Having fulfilled other criteria such as immunogenicity, IFNγ inducing ability, and non-homology to human and microbiome peptides, the epitopes were assembled into a vaccine construct (VC) together with β-defensin as adjuvant and appropriate linkers. The VC was shown to have good physicochemical characteristics and capability of inducing CTL as well as HTL responses, which stem from the engagement of the vaccine with toll-like receptor 4 (TLR4) as revealed by docking simulations. The most promiscuous peptide 899WSMATYYLF907 was shown via docking simulation to interact well with HLA-A*24:07, the most predominant allele in Indonesia. The data presented here will contribute to the in vitro study of T-cell epitope mapping and vaccine design in Indonesia.

Self-mediated positive selection of T cells sets an obstacle to the recognition of nonself

Adaptive immune recognition is mediated by the binding of peptide-human leukocyte antigen complexes by T cells. Positive selection of T cells in the thymus is a fundamental step in the generation of a responding T cell repertoire: only those T cells survive that recognize human peptides presented on the surface of cortical thymic epithelial cells. We propose that while this step is essential for optimal immune function, the process results in a defective T cell repertoire because it is mediated by self-peptides. To test our hypothesis, we focused on amino acid motifs of peptides in contact with T cell receptors. We found that motifs rarely or not found in the human proteome are unlikely to be recognized by the immune system just like the ones that are not expressed in cortical thymic epithelial cells or not presented on their surface. Peptides carrying such motifs were especially dissimilar to human proteins. Importantly, we present our main findings on two independent T cell activation datasets and directly demonstrate the absence of naïve T cells in the repertoire of healthy individuals. We also show that T cell cross-reactivity is unable to compensate for the absence of positively selected T cells. Additionally, we show that the proposed mechanism could influence the risk for different infectious diseases. In sum, our results suggest a side effect of T cell positive selection, which could explain the nonresponsiveness to many nonself peptides and could improve the understanding of adaptive immune recognition.

Tumor infiltrating lymphocytes (TIL) therapy in metastatic melanoma: boosting of neoantigen-specific T cell reactivity and long-term follow-up

Treatment of metastatic melanoma with autologous tumor infiltrating lymphocytes (TILs) is currently applied in several centers. Robust and remarkably consistent overall response rates, of around 50% of treated patients, have been observed across hospitals, including a substantial fraction of durable, complete responses.

PURPOSE

Execute a phase I/II feasibility study with TIL therapy in metastatic melanoma at the Netherlands Cancer Institute, with the goal to assess feasibility and potential value of a randomized phase III trial.

EXPERIMENTAL

Ten patients were treated with TIL therapy. Infusion products and peripheral blood samples were phenotypically characterized and neoantigen reactivity was assessed. Here, we present long-term clinical outcome and translational data on neoantigen reactivity of the T cell products.

RESULTS

Five out of 10 patients, who were all anti-PD-1 naïve at time of treatment, showed an objective clinical response, including two patients with a complete response that are both ongoing for more than 7 years. Immune monitoring demonstrated that neoantigen-specific T cells were detectable in TIL infusion products from three out of three patients analyzed. For six out of the nine neoantigen-specific T cell responses detected in these TIL products, T cell response magnitude increased significantly in the peripheral blood compartment after therapy, and neoantigen-specific T cells were detectable for up to 3 years after TIL infusion.

CONCLUSION

The clinical results from this study confirm the robustness of TIL therapy in metastatic melanoma and the potential role of neoantigen-specific T cell reactivity. In addition, the data from this study supported the rationale to initiate an ongoing multicenter phase III TIL trial.

Structure-guided T cell vaccine design for SARS-CoV-2 variants and sarbecoviruses

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants that escape convalescent and vaccine-induced antibody responses has renewed focus on the development of broadly protective T-cell-based vaccines. Here, we apply structure-based network analysis and assessments of HLA class I peptide stability to define mutationally constrained CD8+ T cell epitopes across the SARS-CoV-2 proteome. Highly networked residues are conserved temporally among circulating variants and sarbecoviruses and disproportionately impair spike pseudotyped lentivirus infectivity when mutated. Evaluation of HLA class I stabilizing activity for 18 globally prevalent alleles identifies CD8+ T cell epitopes within highly networked regions with limited mutational frequencies in circulating SARS-CoV-2 variants and deep-sequenced primary isolates. Moreover, these epitopes elicit demonstrable CD8+ T cell reactivity in convalescent individuals but reduced recognition in recipients of mRNA-based vaccines. These data thereby elucidate key mutationally constrained regions and immunogenic epitopes in the SARS-CoV-2 proteome for a global T-cell-based vaccine against emerging variants and SARS-like coronaviruses.

Borrelia Infection in Bank Voles Myodes glareolus Is Associated With Specific DQB Haplotypes Which Affect Allelic Divergence Within Individuals

The high polymorphism of Major Histocompatibility Complex (MHC) genes is generally considered to be a result of pathogen-mediated balancing selection. Such selection may operate in the form of heterozygote advantage, and/or through specific MHC allele–pathogen interactions. Specific MHC allele–pathogen interactions may promote polymorphism via negative frequency-dependent selection (NFDS), or selection that varies in time and/or space because of variability in the composition of the pathogen community (fluctuating selection; FS). In addition, divergent allele advantage (DAA) may act on top of these forms of balancing selection, explaining the high sequence divergence between MHC alleles. DAA has primarily been thought of as an extension of heterozygote advantage. However, DAA could also work in concert with NFDS though this is yet to be tested explicitly. To evaluate the importance of DAA in pathogen-mediated balancing selection, we surveyed allelic polymorphism of MHC class II DQB genes in wild bank voles (Myodes glareolus) and tested for associations between DQB haplotypes and infection by Borrelia afzelii, a tick-transmitted bacterium causing Lyme disease in humans. We found two significant associations between DQB haplotypes and infection status: one haplotype was associated with lower risk of infection (resistance), while another was associated with higher risk of infection (susceptibility). Interestingly, allelic divergence within individuals was higher for voles with the resistance haplotype compared to other voles. In contrast, allelic divergence was lower for voles with the susceptibility haplotype than other voles. The pattern of higher allelic divergence in individuals with the resistance haplotype is consistent with NFDS favouring divergent alleles in a natural population, hence selection where DAA works in concert with NFDS.

Strength and Numbers: The Role of Affinity and Avidity in the 'Quality' of T Cell Tolerance

The ability of T cells to identify foreign antigens and mount an efficient immune response while limiting activation upon recognition of self and self-associated peptides is critical. Multiple tolerance mechanisms work in concert to prevent the generation and activation of self-reactive T cells. T cell tolerance is tightly regulated, as defects in these processes can lead to devastating disease; a wide variety of autoimmune diseases and, more recently, adverse immune-related events associated with checkpoint blockade immunotherapy have been linked to a breakdown in T cell tolerance. The quantity and quality of antigen receptor signaling depend on a variety of parameters that include T cell receptor affinity and avidity for peptide. Autoreactive T cell fate choices (e.g., deletion, anergy, regulatory T cell development) are highly dependent on the strength of T cell receptor interactions with self-peptide. However, less is known about how differences in the strength of T cell receptor signaling during differentiation influences the 'function' and persistence of anergic and regulatory T cell populations. Here, we review the literature on this subject and discuss the clinical implications of how T cell receptor signal strength influences the 'quality' of anergic and regulatory T cell populations.

Unique pathogen peptidomes facilitate pathogen-specific selection and specialization of MHC alleles

A key component of pathogen-specific adaptive immunity in vertebrates is the presentation of pathogen-derived antigenic peptides by major histocompatibility complex (MHC) molecules. The excessive polymorphism observed at MHC genes is widely presumed to result from the need to recognize diverse pathogens, a process called pathogen-driven balancing selection. This process assumes that pathogens differ in their peptidomes—the pool of short peptides derived from the pathogen’s proteome—so that different pathogens select for different MHC variants with distinct peptide-binding properties. Here, we tested this assumption in a comprehensive data set of 51.9 Mio peptides, derived from the peptidomes of 36 representative human pathogens. Strikingly, we found that 39.7% of the 630 pairwise comparisons among pathogens yielded not a single shared peptide and only 1.8% of pathogen pairs shared more than 1% of their peptides. Indeed, 98.8% of all peptides were unique to a single pathogen species. Using computational binding prediction to characterize the binding specificities of 321 common human MHC class-I variants, we investigated quantitative differences among MHC variants with regard to binding peptides from distinct pathogens. Our analysis showed signatures of specialization toward specific pathogens especially by MHC variants with narrow peptide-binding repertoires. This supports the hypothesis that such fastidious MHC variants might be maintained in the population because they provide an advantage against particular pathogens. Overall, our results establish a key selection factor for the excessive allelic diversity at MHC genes observed in natural populations and illuminate the evolution of variable peptide-binding repertoires among MHC variants.

Targeting public neoantigens for cancer immunotherapy

Several current immunotherapy approaches target private neoantigens derived from mutations that are unique to individual patients' tumors. However, immunotherapeutic agents can also be developed against public neoantigens derived from recurrent mutations in cancer driver genes. The latter approaches target proteins that are indispensable for tumor growth, and each therapeutic agent can be applied to numerous patients. Here we review the opportunities and challenges involved in the identification of suitable public neoantigen targets and the development of therapeutic agents targeting them.

In silico T cell epitope identification for SARS-CoV-2: Progress and perspectives

Growing evidence suggests that T cells may play a critical role in combating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Hence, COVID-19 vaccines that can elicit a robust T cell response may be particularly important. The design, development and experimental evaluation of such vaccines is aided by an understanding of the landscape of T cell epitopes of SARS-CoV-2, which is largely unknown. Due to the challenges of identifying epitopes experimentally, many studies have proposed the use of in silico methods. Here, we present a review of the in silico methods that have been used for the prediction of SARS-CoV-2 T cell epitopes. These methods employ a diverse set of technical approaches, often rooted in machine learning. A performance comparison is provided based on the ability to identify a specific set of immunogenic epitopes that have been determined experimentally to be targeted by T cells in convalescent COVID-19 patients, shedding light on the relative performance merits of the different approaches adopted by the in silico studies. The review also puts forward perspectives for future research directions.

Proteasome-Generated cis-Spliced Peptides and Their Potential Role in CD8+ T Cell Tolerance

The human immune system relies on the capability of CD8+ T cells to patrol body cells, spot infected cells and eliminate them. This cytotoxic response is supposed to be limited to infected cells to avoid killing of healthy cells. To enable this, CD8+ T cells have T Cell Receptors (TCRs) which should discriminate between self and non-self through the recognition of antigenic peptides bound to Human Leukocyte Antigen class I (HLA-I) complexes-i.e., HLA-I immunopeptidomes-of patrolled cells. The majority of these antigenic peptides are produced by proteasomes through either peptide hydrolysis or peptide splicing. Proteasome-generated cis-spliced peptides derive from a given antigen, are immunogenic and frequently presented by HLA-I complexes. Theoretically, they also have a very large sequence variability, which might impinge upon our model of self/non-self discrimination and central and peripheral CD8+ T cell tolerance. Indeed, a large variety of cis-spliced epitopes might enlarge the pool of viral-human zwitter epitopes, i.e., peptides that may be generated with the exact same sequence from both self (human) and non-self (viral) antigens. Antigenic viral-human zwitter peptides may be recognized by CD8+ thymocytes and T cells, induce clonal deletion or other tolerance processes, thereby restraining CD8+ T cell response against viruses. To test this hypothesis, we computed in silico the theoretical frequency of zwitter non-spliced and cis-spliced epitope candidates derived from human proteome (self) and from the proteomes of a large pool of viruses (non-self). We considered their binding affinity to the representative HLA-A*02:01 complex, self-antigen expression in Medullary Thymic Epithelial cells (mTECs) and the relative frequency of non-spliced and cis-spliced peptides in HLA-I immunopeptidomes. Based on the present knowledge of proteasome-catalyzed peptide splicing and neglecting CD8+ TCR degeneracy, our study suggests that, despite their frequency, the portion of the cis-spliced peptides we investigated could only marginally impinge upon the variety of functional CD8+ cytotoxic T cells (CTLs) involved in anti-viral response.

Computational design of SARS-CoV-2 spike glycoproteins to increase immunogenicity by T cell epitope engineering

The development of effective and safe vaccines is the ultimate way to efficiently stop the ongoing COVID-19 pandemic, which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Built on the fact that SARS-CoV-2 utilizes the association of its Spike (S) protein with the human angiotensin-converting enzyme 2 (ACE2) receptor to invade host cells, we computationally redesigned the S protein sequence to improve its immunogenicity and antigenicity. Toward this purpose, we extended an evolutionary protein design algorithm, EvoDesign, to create thousands of stable S protein variants that perturb the core protein sequence but keep the surface conformation and B cell epitopes. The T cell epitope content and similarity scores of the perturbed sequences were calculated and evaluated. Out of 22,914 designs with favorable stability energy, 301 candidates contained at least two pre-existing immunity-related epitopes and had promising immunogenic potential. The benchmark tests showed that, although the epitope restraints were not included in the scoring function of EvoDesign, the top S protein design successfully recovered 31 out of the 32 major histocompatibility complex (MHC)-II T cell promiscuous epitopes in the native S protein, where two epitopes were present in all seven human coronaviruses. Moreover, the newly designed S protein introduced nine new MHC-II T cell promiscuous epitopes that do not exist in the wildtype SARS-CoV-2. These results demonstrated a new and effective avenue to enhance a target protein's immunogenicity using rational protein design, which could be applied for new vaccine design against COVID-19 and other pathogens.

Solving an MHC allele-specific bias in the reported immunopeptidome

Identification of MHC class I–bound peptides by immunopurification of MHC complexes and subsequent analysis by mass spectrometry is crucial for understanding T cell immunology and immunotherapy. Investigation of the steps for the MHC ligand isolation process revealed biases in widely used isolation techniques toward peptides of lower hydrophobicity. As MHC ligand hydrophobicity correlates positively with immunogenicity, identification of more hydrophobic MHC ligands could potentially lead to more effective isolation of immunogenic peptides as targets for immunotherapies. We solved this problem by use of higher concentrations of acetonitrile for the separation of MHC ligands and their respective complexes. This increased overall MHC ligand identifications by 2-fold, increased detection of cancer germline antigen–derived peptides by 50%, and resulted in profound variations in isolation efficacy between different MHC alleles correlating with the hydrophobicity of their anchor residues. Overall, these insights enabled a more complete view of the immunopeptidome and overcame a systematic underrepresentation of these critical MHC ligands of high hydrophobicity.

An approach is identified to prevent bias in the immunopeptidome towards MHC ligands of lower hydrophobicity and therefore immunogenicity.

Rational Design of SARS-CoV-2 Spike Glycoproteins To Increase Immunogenicity By T Cell Epitope Engineering

The current COVID-19 pandemic caused by SARS-CoV-2 has resulted in millions of confirmed cases and thousands of deaths globally. Extensive efforts and progress have been made to develop effective and safe vaccines against COVID-19. A primary target of these vaccines is the SARS-CoV-2 spike (S) protein, and many studies utilized structural vaccinology techniques to either stabilize the protein or fix the receptor-binding domain at certain states. In this study, we extended an evolutionary protein design algorithm, EvoDesign, to create thousands of stable S protein variants without perturbing the surface conformation and B cell epitopes of the S protein. We then evaluated the mutated S protein candidates based on predicted MHC-II T cell promiscuous epitopes as well as the epitopes' similarity to human peptides. The presented strategy aims to improve the S protein's immunogenicity and antigenicity by inducing stronger CD4 T cell response while maintaining the protein's native structure and function. The top EvoDesign S protein candidate (Design-10705) recovered 31 out of 32 MHC-II T cell promiscuous epitopes in the native S protein, in which two epitopes were present in all seven human coronaviruses. This newly designed S protein also introduced nine new MHC-II T cell promiscuous epitopes and showed high structural similarity to its native conformation. The proposed structural vaccinology method provides an avenue to rationally design the antigen's structure with increased immunogenicity, which could be applied to the rational design of new COVID-19 vaccine candidates.

Identifying the immune interactions underlying HLA class I disease associations

Variation in the risk and severity of many autoimmune diseases, malignancies and infections is strongly associated with polymorphisms at the HLA class I loci. These genetic associations provide a powerful opportunity for understanding the etiology of human disease. HLA class I associations are often interpreted in the light of 'protective' or 'detrimental' CD8⁺ T cell responses which are restricted by the host HLA class I allotype. However, given the diverse receptors which are bound by HLA class I molecules, alternative interpretations are possible. As well as binding T cell receptors on CD8⁺ T cells, HLA class I molecules are important ligands for inhibitory and activating killer immunoglobulin-like receptors (KIRs) which are found on natural killer cells and some T cells; for the CD94:NKG2 family of receptors also expressed mainly by NK cells and for leukocyte immunoglobulin-like receptors (LILRs) on myeloid cells. The aim of this study is to develop an immunogenetic approach for identifying and quantifying the relative contribution of different receptor-ligand interactions to a given HLA class I disease association and then to use this approach to investigate the immune interactions underlying HLA class I disease associations in three viral infections: Human T cell Leukemia Virus type 1, Human Immunodeficiency Virus type 1 and Hepatitis C Virus as well as in the inflammatory condition Crohn's disease.

Is T Cell Negative Selection a Learning Algorithm?

Our immune system can destroy most cells in our body, an ability that needs to be tightly controlled. To prevent autoimmunity, the thymic medulla exposes developing T cells to normal “self” peptides and prevents any responders from entering the bloodstream. However, a substantial number of self-reactive T cells nevertheless reaches the periphery, implying that T cells do not encounter all self peptides during this negative selection process. It is unclear if T cells can still discriminate foreign peptides from self peptides they haven’t encountered during negative selection. We use an “artificial immune system”—a machine learning model of the T cell repertoire—to investigate how negative selection could alter the recognition of self peptides that are absent from the thymus. Our model reveals a surprising new role for T cell cross-reactivity in this context: moderate T cell cross-reactivity should skew the post-selection repertoire towards peptides that differ systematically from self. Moreover, even some self-like foreign peptides can be distinguished provided that the peptides presented in the thymus are not too similar to each other. Thus, our model predicts that negative selection on a well-chosen subset of self peptides would generate a repertoire that tolerates even “unseen” self peptides better than foreign peptides. This effect would resemble a “generalization” process as it is found in learning systems. We discuss potential experimental approaches to test our theory.

Identification of HCMV-derived T cell epitopes in seropositive individuals through viral deletion models

In healthy individuals, immune control of persistent human cytomegalovirus (HCMV) infection is effectively mediated by virus-specific CD4+ and CD8+ T cells. However, identifying the repertoire of T cell specificities for HCMV is hampered by the immense protein coding capacity of this betaherpesvirus. Here, we present a novel approach that employs HCMV deletion mutant viruses lacking HLA class I immunoevasins and allows direct identification of naturally presented HCMV-derived HLA ligands by mass spectrometry. We identified 368 unique HCMV-derived HLA class I ligands representing an unexpectedly broad panel of 123 HCMV antigens. Functional characterization revealed memory T cell responses in seropositive individuals for a substantial proportion (28%) of these novel peptides. Multiple HCMV-directed specificities in the memory T cell pool of single individuals indicate that physiologic anti-HCMV T cell responses are directed against a broad range of antigens. Thus, the unbiased identification of naturally presented viral epitopes enabled a comprehensive and systematic assessment of the physiological repertoire of anti-HCMV T cell specificities in seropositive individuals.

Viral Hormones: Expanding Dimensions in Endocrinology

Viruses have developed different mechanisms to manipulate their hosts, including the process of viral mimicry in which viruses express important host proteins. Until recently, examples of viral mimicry were limited to mimics of growth factors and immunomodulatory proteins. Using a comprehensive bioinformatics approach, we have shown that viruses possess the DNA/RNA with potential to encode 16 different peptides with high sequence similarity to human peptide hormones and metabolically important regulatory proteins. We have characterized one of these families, the viral insulin/IGF-1-like peptides (VILPs), which we identified in four members of the Iridoviridae family. VILPs can bind to human insulin and IGF-1 receptors and stimulate classic postreceptor signaling pathways. Moreover, VILPs can stimulate glucose uptake in vitro and in vivo and stimulate DNA synthesis. DNA sequences of some VILP-carrying viruses have been identified in the human enteric virome. In addition to VILPs, sequences with homology to 15 other peptide hormones or cytokines can be identified in viral DNA/RNA sequences, some with a very high identity to hormones. Recent data by others has identified a peptide that resembles and mimics α-melanocyte-stimulating hormone's anti-inflammatory effects in in vitro and in vivo models. Taken together, these studies reveal novel mechanisms of viral and bacterial pathogenesis in which the microbe can directly target or mimic the host endocrine system. These findings also introduce the concept of a system of microbial hormones that provides new insights into the evolution of peptide hormones, as well as potential new roles of microbial hormones in health and disease.

Bioinformatic methods for cancer neoantigen prediction

Tumor cells accumulate aberrations not present in normal cells, leading to presentation of neoantigens on MHC molecules on their surface. These non-self neoantigens distinguish tumor cells from normal cells to the immune system and are thus targets for cancer immunotherapy. The rapid development of molecular profiling platforms, such as next-generation sequencing, has enabled the generation of large datasets characterizing tumor cells. The simultaneous development of algorithms has enabled rapid and accurate processing of these data. Bioinformatic software tools encoding the algorithms can be strung together in a workflow to identify neoantigens. Here, with a focus on high-throughput sequencing, we review state-of-the art bioinformatic tools along with the steps and challenges involved in neoantigen identification and recognition.

Balancing sensitivity and specificity in distinguishing TCR groups by CDR sequence similarity

Background

Repertoire sequencing is enabling deep explorations into the cellular immune response, including the characterization of commonalities and differences among T cell receptor (TCR) repertoires from different individuals, pathologies, and antigen specificities. In seeking to understand the generality of patterns observed in different groups of TCRs, it is necessary to balance how well each pattern represents the diversity among TCRs from one group (sensitivity) vs. how many TCRs from other groups it also represents (specificity). The variable complementarity determining regions (CDRs), particularly the third CDRs (CDR3s) interact with major histocompatibility complex (MHC)-presented epitopes from putative antigens, and thus encode the determinants of recognition.

Results

We here systematically characterize the predictive power that can be obtained from CDR3 sequences, using representative, readily interpretable methods for evaluating CDR sequence similarity and then clustering and classifying sequences based on similarity. An initial analysis of CDR3s of known structure, clustered by structural similarity, helps calibrate the limits of sequence diversity among CDRs that might have a common mode of interaction with presented epitopes. Subsequent analyses demonstrate that this same range of sequence similarity strikes a favorable specificity/sensitivity balance in distinguishing twins from non-twins based on overall CDR3 repertoires, classifying CDR3 repertoires by antigen specificity, and distinguishing general pathologies.

Conclusion

We conclude that within a fairly broad range of sequence similarity, matching CDR3 sequences are likely to share specificities.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2864-8) contains supplementary material, which is available to authorized users.

Structural topology defines protective CD8+ T cell epitopes in the HIV proteome

Mutationally constrained epitopes of variable pathogens represent promising targets for vaccine design but are not reliably identified by sequence conservation. In this study, we employed structure-based network analysis, which applies network theory to HIV protein structure data to quantitate the topological importance of individual amino acid residues. Mutation of residues at important network positions disproportionately impaired viral replication and occurred with high frequency in epitopes presented by protective human leukocyte antigen (HLA) class I alleles. Moreover, CD8+ T cell targeting of highly networked epitopes distinguished individuals who naturally control HIV, even in the absence of protective HLA alleles. This approach thereby provides a mechanistic basis for immune control and a means to identify CD8+ T cell epitopes of topological importance for rational immunogen design, including a T cell-based HIV vaccine.

Immune aging in diabetes and its implications in wound healing

Immune systems have evolved to recognize and eliminate pathogens and damaged cells. In humans, it is estimated to recognize 109 epitopes and natural selection ensures that clonally expanded cells replace unstimulated cells and overall immune cell numbers remain stationary. But, with age, it faces continuous repertoire restriction and concomitant accumulation of primed cells. Changes shaping the aging immune system have bitter consequences because, as inflammatory responses gain intensity and duration, tissue-damaging immunity and inflammatory disease arise. During inflammation, the glycolytic flux cannot cope with increasing ATP demands, limiting the immune response's extent. In diabetes, higher glucose availability stretches the glycolytic limit, dysregulating proteostasis and increasing T-cell expansion. Long-term hyperglycemia exerts an accumulating effect, leading to higher inflammatory cytokine levels and increased cytotoxic mediator secretion upon infection, a phenomenon known as diabetic chronic inflammation. Here we review the etiology of diabetic chronic inflammation and its consequences on wound healing.

Cancer Neoantigens

Malignant transformation of cells depends on accumulation of DNA damage. Over the past years we have learned that the T cell-based immune system frequently responds to the neoantigens that arise as a consequence of this DNA damage. Furthermore, recognition of neoantigens appears an important driver of the clinical activity of both T cell checkpoint blockade and adoptive T cell therapy as cancer immunotherapies. Here we review the evidence for the relevance of cancer neoantigens in tumor control and the biological properties of these antigens. We discuss recent technological advances utilized to identify neoantigens, and the T cells that recognize them, in individual patients. Finally, we discuss strategies that can be employed to exploit cancer neoantigens in clinical interventions.

The expanding role of systems immunology in decoding the T cell receptor repertoire

T cells play a crucial role in the immune system's defense against many infectious diseases, including persistent infections for which no effective vaccines currently exist. The T cell component of the adaptive immune system is highly complex involving a constantly evolving landscape of various inter-related T cell populations. These T cell populations are characterized by their phenotypic and functional properties as well as the collection, or repertoire, of T cell receptors (TCR) that mediate T cell recognition of antigenic peptides derived from pathogens. Understanding the various processes and factors that impact the development and evolution of the broader T cell repertoire available to recognize and respond to pathogens and the characteristics of antigen-experienced T cell repertoires associated with effective immune control of pathogens is critical to the rational design of T cell-based vaccines and therapies. In this article we discuss, using examples of recent research, the promise that systems immunology approaches, involving quantitative analysis and mathematical and computational modeling of immunological data, hold for decoding the complex TCR repertoire system in the current era of advancing technologies.

Pan-cancer analysis of neoepitopes

Somatic variations are frequent and important drivers in cancers. Amino acid substitutions can yield neoantigens that are detected by the immune system. Neoantigens can lead to immune response and tumor rejection. Although neoantigen load and occurrence have been widely studied, a detailed pan-cancer analysis of the occurrence and characterization of neoepitopes is missing. We investigated the proteome-wide amino acid substitutions in 8-, 9-, 10-, and 11-mer peptides in 30 cancer types with the NetMHC 4.0 software. 11,316,078 (0.24%) of the predicted 8-, 9-, 10-, and 11-mer peptides were highly likely neoepitope candidates and were derived from 95.44% of human proteins. Binding affinity to MHC molecules is just one of the many epitope features. The most likely epitopes are those which are detected by several MHCs and of several peptide lengths. 9-mer peptides are the most common among the high binding neoantigens. 0.17% of all variants yield more than 100 neoepitopes and are considered as the best candidates for any application. Amino acid distributions indicate that variants at all positions in neoepitopes of any length are, on average, more hydrophobic than the wild-type residues. We characterized properties of neoepitopes in 30 cancer types and estimated the likely numbers of tumor-derived epitopes that could induce an immune response. We found that amino acid distributions, at all positions in neoepitopes of all lengths, contain more hydrophobic residues than the wild-type sequences implying that the hydropathy nature of neoepitopes is an important property. The neoepitope characteristics can be employed for various applications including targeted cancer vaccine development for precision medicine.

Why do proteases mess up with antigen presentation by re-shuffling antigen sequences?

The sequence of a large number of MHC-presented epitopes is not present as such in the original antigen because it has been re-shuffled by the proteasome or other proteases. Why do proteases throw a spanner in the works of our model of antigen tagging and immune recognition? We describe in this review what we know about the immunological relevance of post-translationally spliced epitopes and why proteases seem to have a second (dark) personality, which is keen to create new peptide bonds.

Viral peptides-MHC interaction: Binding probability and distance from human peptides

Identification of peptides binding to MHC class I complex can play a crucial role in retrieving potential targets able to trigger an immune response. Affinity binding of viral peptides can be estimated through effective computational methods that in the most of cases are based on machine learning approach. Achieving a better insight into peptide features that impact on the affinity binding rate is a challenging issue. In the present work we focused on 9-mer peptides of Human immunodeficiency virus type 1 and Human herpes simplex virus 1, studying their binding to MHC class I. Viral 9-mers were partitioned into different classes, where each class is characterized by how far (in terms of mutation steps) the peptides belonging to that class are from human 9-mers. Viral 9-mers were partitioned in different classes, based on the number of mutation steps they are far from human 9-mers. We showed that the overall binding probability significantly differs among classes, and it typically increases as the distance, computed in terms of number of mutation steps from the human set of 9-mers, increases. The binding probability is particularly high when considering viral 9-mers that are far from all human 9-mers more than three mutation steps. A further evidence, providing significance to those special viral peptides and suggesting a potential role they can play, comes from the analysis of their distribution along viral genomes, as it revealed they are not randomly located, but they preferentially occur in specific genes.

Regulation of Cytokine Production by the Unfolded Protein Response; Implications for Infection and Autoimmunity

Protein folding in the endoplasmic reticulum (ER) is an essential cell function. To safeguard this process in the face of environmental threats and internal stressors, cells mount an evolutionarily conserved response known as the unfolded protein response (UPR). Invading pathogens induce cellular stress that impacts protein folding, thus the UPR is well situated to sense danger and contribute to immune responses. Cytokines (inflammatory cytokines and interferons) critically mediate host defense against pathogens, but when aberrantly produced, may also drive pathologic inflammation. The UPR influences cytokine production on multiple levels, from stimulation of pattern recognition receptors, to modulation of inflammatory signaling pathways, and the regulation of cytokine transcription factors. This review will focus on the mechanisms underlying cytokine regulation by the UPR, and the repercussions of this relationship for infection and autoimmune/autoinflammatory diseases. Interrogation of viral and bacterial infections has revealed increasing numbers of examples where pathogens induce or modulate the UPR and implicated UPR-modulated cytokines in host response. The flip side of this coin, the UPR/ER stress responses have been increasingly recognized in a variety of autoimmune and inflammatory diseases. Examples include monogenic disorders of ER function, diseases linked to misfolding protein (HLA-B27 and spondyloarthritis), diseases directly implicating UPR and autophagy genes (inflammatory bowel disease), and autoimmune diseases targeting highly secretory cells (e.g., diabetes). Given the burgeoning interest in pharmacologically targeting the UPR, greater discernment is needed regarding how the UPR regulates cytokine production during specific infections and autoimmune processes, and the relative place of this interaction in pathogenesis.

Analyzing the effect of peptide-HLA-binding ability on the immunogenicity of potential CD8+ and CD4+ T cell epitopes in a large dataset

Immunogenicity is a key factor that influences whether a peptide presented by major histocompatibility complex (MHC) can be a T cell epitope. However, peptide immunization experiments have shown that approximately half of MHC class I-binding peptides cannot elicit a T cell response, indicating the importance of analyzing the variables affecting the immunogenicity of MHC-binding peptides. In this study, we hierarchically investigated the contribution of the binding stability and affinity of peptide-MHC complexes to immunogenicity based on the available quantitative data. We found that the immunogenicity of peptides presented by human leukocyte antigen (HLA) class I molecules was still predictable using the experimental binding affinity, although approximately one-third of the peptides with a binding affinity stronger than 500 nM were non-immunogenic, whereas the immunogenicity of HLA-II-presented peptides was predicted well using the experimental affinity and even the predicted affinity. The positive correlation between the binding affinity and stability was only observed in peptide-HLA-I complexes with a binding affinity stronger than 500 nM, which suggested that the stability alone could not be used for the prediction of immunogenicity. A characterization and comparison of the 'holes' in the CD8+ and CD4+ T cell repertoire provided an explanation for the observed differences between the immunogenicity of peptides presented by HLA class I and II molecules. We also provided the optimal affinity threshold for the potential CD4+ and CD8+ T cell epitopes. Our results provide important insights into the cellular immune response and the accurate prediction of T cell epitopes.

In Silico Prediction Analysis of Idiotope-Driven T-B Cell Collaboration in Multiple Sclerosis

Memory B cells acting as antigen-presenting cells are believed to be important in multiple sclerosis (MS), but the antigen they present remains unknown. We hypothesized that B cells may activate CD4⁺ T cells in the central nervous system of MS patients by presenting idiotopes from their own immunoglobulin variable regions on human leukocyte antigen (HLA) class II molecules. Here, we use bioinformatics prediction analysis of B cell immunoglobulin variable regions from 11 MS patients and 6 controls with other inflammatory neurological disorders (OINDs), to assess whether the prerequisites for such idiotope-driven T–B cell collaboration are present. Our findings indicate that idiotopes from the complementarity determining region (CDR) 3 of MS patients on average have high predicted affinities for disease associated HLA-DRB1*15:01 molecules and are predicted to be endosomally processed by cathepsin S and L in positions that allows such HLA binding to occur. Additionally, complementarity determining region 3 sequences from cerebrospinal fluid (CSF) B cells from MS patients contain on average more rare T cell-exposed motifs that could potentially escape tolerance and stimulate CD4⁺ T cells than CSF B cells from OIND patients. Many of these features were associated with preferential use of the IGHV4 gene family by CSF B cells from MS patients. This is the first study to combine high-throughput sequencing of patient immune repertoires with large-scale prediction analysis and provides key indicators for future in vitro and in vivo analyses.

Post-Translational Peptide Splicing and T Cell Responses

CD8⁺ T cell specificity depends on the recognition of MHC class I-epitope complexes at the cell surface. These epitopes are mainly produced via degradation of proteins by the proteasome, generating fragments of the original sequence. However, it is now clear that proteasomes can produce a significant portion of epitopes by reshuffling the antigen sequence, thus expanding the potential antigenic repertoire. MHC class I-restricted spliced epitopes have been described in tumors and infections, suggesting an unpredicted relevance of these peculiar peptides. We review current knowledge about proteasome-catalyzed peptide splicing (PCPS), the emerging rules governing this process, and the potential implications for our understanding and therapeutic use of CD8⁺ T cells, as well as mechanisms generating other non-canonical antigenic epitopes targeted by the T cell response.

Multi-level Strategy for Identifying Proteasome-Catalyzed Spliced Epitopes Targeted by CD8+ T Cells during Bacterial Infection

Proteasome-catalyzed peptide splicing (PCPS) generates peptides that are presented by MHC class I molecules, but because their identification is challenging, the immunological relevance of spliced peptides remains unclear. Here, we developed a reverse immunology-based multi-level approach to identify proteasome-generated spliced epitopes. Applying this strategy to a murine Listeria monocytogenes infection model, we identified two spliced epitopes within the secreted bacterial phospholipase PlcB that primed antigen-specific CD8+ T cells in L. monocytogenes-infected mice. While reacting to the spliced epitopes, these CD8+ T cells failed to recognize the non-spliced peptide parts in the context of their natural flanking sequences. Thus, we here show that PCPS expands the CD8+ T cell response against L. monocytogenes by exposing spliced epitopes on the cell surface. Moreover, our multi-level strategy opens up opportunities to systematically investigate proteins for spliced epitope candidates and thus strategies for immunotherapies or vaccine design.

Converging evolution leads to near maximal junction diversity through parallel mechanisms in B and T cell receptors

T and B cell receptor (TCR and BCR) complementarity determining region 3 (CDR3) genetic diversity is produced through multiple diversification and selection stages. Potential holes in the CDR3 repertoire were argued to be linked to immunodeficiencies and diseases. In contrast with BCRs, TCRs have practically no Dβ germline genetic diversity, and the question emerges as to whether they can produce a diverse CDR3 repertoire. In order to address the genetic diversity of the adaptive immune system, appropriate quantitative measures for diversity and large-scale sequencing are required. Such a diversity method should incorporate the complex diversification mechanisms of the adaptive immune response and the BCR and TCR loci structure. We combined large-scale sequencing and diversity measures to show that TCRs have a near maximal CDR3 genetic diversity. Specifically, TCR have a larger junctional and V germline diversity, which starts more 5' in Vβ than BCRs. Selection decreases the TCR repertoire diversity, but does not affect BCR repertoire. As a result, TCR is as diverse as BCR repertoire, with a biased CDR3 length toward short TCRs and long BCRs. These differences suggest parallel converging evolutionary tracks to reach the required diversity to avoid holes in the CDR3 repertoire.

Quantification of degeneracy in Hodgkin-Huxley neurons on Newman-Watts small world network

Degeneracy is a fundamental source of biological robustness, complexity and evolvability in many biological systems. However, degeneracy is often confused with redundancy. Furthermore, the quantification of degeneracy has not been addressed for realistic neuronal networks. The objective of this paper is to characterize degeneracy in neuronal network models via quantitative mathematic measures. Firstly, we establish Hodgkin-Huxley neuronal networks with Newman-Watts small world network architectures. Secondly, in order to calculate the degeneracy, redundancy and complexity in the ensuing networks, we use information entropy to quantify the information a neuronal response carries about the stimulus - and mutual information to measure the contribution of each subset of the neuronal network. Finally, we analyze the interdependency of degeneracy, redundancy and complexity - and how these three measures depend upon network architectures. Our results suggest that degeneracy can be applied to any neuronal network as a formal measure, and degeneracy is distinct from redundancy. Qualitatively degeneracy and complexity are more highly correlated over different network architectures, in comparison to redundancy. Quantitatively, the relationship between both degeneracy and redundancy depends on network coupling strength: both degeneracy and redundancy increase with complexity for small coupling strengths; however, as coupling strength increases, redundancy decreases with complexity (in contrast to degeneracy, which is relatively invariant). These results suggest that the degeneracy is a general topologic characteristic of neuronal networks, which could be applied quantitatively in neuroscience and connectomics.

T-cell recognition is shaped by epitope sequence conservation in the host proteome and microbiome

Several mechanisms exist to avoid or suppress inflammatory T-cell immune responses that could prove harmful to the host due to targeting self-antigens or commensal microbes. We hypothesized that these mechanisms could become evident when comparing the immunogenicity of a peptide from a pathogen or allergen with the conservation of its sequence in the human proteome or the healthy human microbiome. Indeed, performing such comparisons on large sets of validated T-cell epitopes, we found that epitopes that are similar with self-antigens above a certain threshold showed lower immunogenicity, presumably as a result of negative selection of T cells capable of recognizing such peptides. Moreover, we also found a reduced level of immune recognition for epitopes conserved in the commensal microbiome, presumably as a result of peripheral tolerance. These findings indicate that the existence (and potentially the polarization) of T-cell responses to a given epitope is influenced and to some extent predictable based on its similarity to self-antigens and commensal antigens.

How many TCR clonotypes does a body maintain?

We consider the lifetime of a T cell clonotype, the set of T cells with the same T cell receptor, from its thymic origin to its extinction in a multiclonal repertoire. Using published estimates of total cell numbers and thymic production rates, we calculate the mean number of cells per TCR clonotype, and the total number of clonotypes, in mice and humans. When there is little peripheral division, as in a mouse, the number of cells per clonotype is small and governed by the number of cells with identical TCR that exit the thymus. In humans, peripheral division is important and a clonotype may survive for decades, during which it expands to comprise many cells. We therefore devise and analyse a computational model of homeostasis of a multiclonal population. Each T cell in the model competes for self pMHC stimuli, cells of any one clonotype only recognising a small fraction of the many subsets of stimuli. A constant mean total number of cells is maintained by a balance between cell division and death, and a stable number of clonotypes by a balance between thymic production of new clonotypes and extinction of existing ones. The number of distinct clonotypes in a human body may be smaller than the total number of naive T cells by only one order of magnitude.

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The number of T cells of one clonotype is an integer.

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The history of a clonotype starts with release from the thymus, and ends with extinction.

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Competition and cross-reactivity are included in a natural way.

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The average number of cells per clonotype, in a human body, is only of order 10.

Hit-and-run, hit-and-stay, and commensal bacteria present different peptide content when viewed from the perspective of the T cell

Different types of bacteria face different pressures from the immune system, with those that persist ("hit-and-stay") potentially having to adapt more in order to escape than those prone to short-lived infection ("hit-and-run"), and with commensal bacteria potentially different from both due to additional physical mechanisms for avoiding immune detection. The Janus Immunogenicity Score (JIS) was recently developed to assess the likelihood of T cell recognition of an antigen, using an analysis that considers both binding of a peptide within the antigen by major histocompatability complex (MHC) and recognition of the peptide:MHC complex by cognate T cell receptor (TCR). This score was shown to be predictive of T effector vs. T regulatory or null responses in experimental data, as well as to distinguish viruses representative of the hit-and-stay vs. hit-and-run phenotypes. Here, JIS-based analyses were conducted in order to characterize the extent to which the pressure to avoid T cell recognition is manifested in genomic differences among representative hit-and-run, hit-and-stay, and commensal bacteria. Overall, extracellular proteins were found to have different JIS profiles from cytoplasmic ones. Contrasting the bacterial groups, extracellular proteins were shown to be quite different across the groups, much more so than intracellular proteins. The differences were evident even at the level of corresponding peptides in homologous protein pairs from hit-and-run and hit-and-stay bacteria. The multi-level analysis of patterns of immunogenicity across different groups of bacteria provides a new way to approach questions of bacterial immune camouflage or escape, as well as to approach the selection and optimization of candidates for vaccine design.

Improved structural method for T-cell cross-reactivity prediction

Cytotoxic T-lymphocytes (CTLs) are the key players of adaptive cellular immunity, being able to identify and eliminate infected cells through the interaction with peptide-loaded major histocompatibility complexes class I (pMHC-I). Despite the high specificity of this interaction, a given lymphocyte is actually able to recognize more than just one pMHC-I complex, a phenomenon referred as cross-reactivity. In the present work we describe the use of pMHC-I structural features as input for multivariate statistical methods, to perform standardized structure-based predictions of cross-reactivity among viral epitopes. Our improved approach was able to successfully identify cross-reactive targets among 28 naturally occurring hepatitis C virus (HCV) variants and among eight epitopes from the four dengue virus serotypes. In both cases, our results were supported by multiscale bootstrap resampling and by data from previously published in vitro experiments. The combined use of data from charges and accessible surface area (ASA) of selected residues over the pMHC-I surface provided a powerful way of assessing the structural features involved in triggering cross-reactive responses. Moreover, the use of an R package (pvclust) for assessing the uncertainty in the hierarchical cluster analysis provided a statistical support for the interpretation of results. Taken together, these methods can be applied to vaccine design, both for the selection of candidates capable of inducing immunity against different targets, or to identify epitopes that could trigger undesired immunological responses.

H7N9 T-cell epitopes that mimic human sequences are less immunogenic and may induce Treg-mediated tolerance

Avian-origin H7N9 influenza is a novel influenza A virus (IAV) that emerged in humans in China in 2013. Using immunoinformatics tools, we identified several H7N9 T cell epitopes with T cell receptor (TCR)-facing residues identical to those of multiple epitopes from human proteins. We hypothesized that host tolerance to these peptides may impair T helper response and contribute to the low titer, weak hemagglutination inhibiting (HI) antibody responses and diminished seroconversion rates that have been observed in human H7N9 infections and vaccine trials. We found that the magnitude of human T effector responses to individual H7N9 peptides was inversely correlated with the peptide's resemblance to self. Furthermore, a promiscuous T cell epitope from the hemagglutinin (HA) protein suppressed responses to other H7N9 peptides when co-administered in vitro. Along with other highly ‘human-like’ peptides from H7N9, this peptide was also shown to expand FoxP3⁺ regulatory T cells (Tregs). Thus, H7N9 may be camouflaged from effective human immune response by T cell epitope sequences that avert or regulate effector T cell responses through host tolerance.

Revisiting thymic positive selection and the mature T cell repertoire for antigen

To support effective host defense, the T cell repertoire must balance breadth of recognition with sensitivity for antigen. The concept that T lymphocytes are positively selected in the thymus is well established, but how this selection achieves such a repertoire has not been resolved. Here we suggest that it is direct linkage between self and foreign antigen recognition that produces the necessary blend of TCR diversity and specificity in the mature peripheral repertoire, enabling responses to a broad universe of unpredictable antigens while maintaining an adequate number of highly sensitive T cells in a population of limited size. Our analysis also helps to explain how diversity and frequency of antigen-reactive cells in a T cell repertoire are adjusted in animals of vastly different size scale to enable effective antipathogen responses and suggests a possible binary architecture in the TCR repertoire that is divided between germline-related optimal binding and diverse recognition.

Lymphocyte repertoire selection and intracellular self/non-self-discrimination: historical overview

Immunological self/non-self-discrimination is conventionally seen as an extracellular event, involving interactions been receptors on T cells pre-educated to discriminate and peptides bound to major histocompatibility complex proteins (pMHCs). Mechanisms by which non-self peptides might first be sorted intracellularly to distinguish them from the vast excess of self-peptides have long been called for. Recent demonstrations of endogenous peptide-specific clustering of pMHCs on membrane rafts are indicative of intracellular enrichment before surface display. The clustering could follow the specific aggregation of a foreign protein that exceeded its solubility limit in the crowded intracellular environment. Predominantly entropy-driven, this homoaggregation would colocalize identical peptides, thus facilitating their collective presentation. Concentrations of self-proteins are fine-tuned over evolutionary time to avoid this. Disparate observations, such as pyrexia and female susceptibility to autoimmune disease, can be explained in terms of the need to cosegregate cognate pMHC complexes internally before extracellular display.

Frequency Patterns of T-Cell Exposed Amino Acid Motifs in Immunoglobulin Heavy Chain Peptides Presented by MHCs

Immunoglobulins are highly diverse protein sequences that are processed and presented to T-cells by B-cells and other antigen presenting cells. We examined a large dataset of immunoglobulin heavy chain variable regions (IGHV) to assess the diversity of T-cell exposed motifs (TCEMs). TCEM comprise those amino acids in a MHC-bound peptide, which face outwards, surrounded by the MHC histotope, and which engage the T-cell receptor. Within IGHV there is a distinct pattern of predicted MHC class II binding and a very high frequency of re-use of the TCEMs. The re-use frequency indicates that only a limited number of different cognate T-cells are required to engage many different clonal B-cells. The amino acids in each outward-facing TCEM are intercalated with the amino acids of inward-facing MHC groove-exposed motifs (GEM). Different GEM may have differing, allele-specific, MHC binding affinities. The intercalation of TCEM and GEM in a peptide allows for a vast combinatorial repertoire of epitopes, each eliciting a different response. Outcome of T-cell receptor binding is determined by overall signal strength, which is a function of the number of responding T-cells and the duration of engagement. Hence, the frequency of TCEM re-use appears to be an important determinant of whether a T-cell response is stimulatory or suppressive. The frequency distribution of TCEMs implies that somatic hypermutation is followed by T-cell clonal expansion that develops along repeated pathways. The observations of TCEM and GEM derived from immunoglobulins suggest a relatively simple, yet powerful, mechanism to correlate T-cell polyspecificity, through re-use of TCEMs, with a very high degree of specificity achieved by combination with a diversity of GEMs. The frequency profile of TCEMs also points to an economical mechanism for maintaining T-cell memory, recall, and self-discrimination based on an endogenously generated profile of motifs.